Package: DSWE 1.8.2
DSWE: Data Science for Wind Energy
Data science methods used in wind energy applications. Current functionalities include creating a multi-dimensional power curve model, performing power curve function comparison, covariate matching, and energy decomposition. Relevant works for the developed functions are: funGP() - Prakash et al. (2022) <doi:10.1080/00401706.2021.1905073>, AMK() - Lee et al. (2015) <doi:10.1080/01621459.2014.977385>, tempGP() - Prakash et al. (2022) <doi:10.1080/00401706.2022.2069158>, ComparePCurve() - Ding et al. (2021) <doi:10.1016/j.renene.2021.02.136>, deltaEnergy() - Latiffianti et al. (2022) <doi:10.1002/we.2722>, syncSize() - Latiffianti et al. (2022) <doi:10.1002/we.2722>, imptPower() - Latiffianti et al. (2022) <doi:10.1002/we.2722>, All other functions - Ding (2019, ISBN:9780429956508).
Authors:
DSWE_1.8.2.tar.gz
DSWE_1.8.2.tar.gz(r-4.5-noble)DSWE_1.8.2.tar.gz(r-4.4-noble)
DSWE_1.8.2.tgz(r-4.4-emscripten)DSWE_1.8.2.tgz(r-4.3-emscripten)
DSWE.pdf |DSWE.html✨
DSWE/json (API)
# Install 'DSWE' in R: |
install.packages('DSWE', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/tamu-aml/dswe-package/issues
Last updated 10 months agofrom:a138270529. Checks:OK: 2. Indexed: no.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Nov 14 2024 |
R-4.5-linux-x86_64 | OK | Nov 14 2024 |
Exports:AMKComparePCurveComputeWeightedDifferenceCovMatchdeltaEnergyfunGPimptPowerKnnPCFitKnnPredictKnnUpdateSplinePCFitSvmPCFitsyncSizetempGPupdateDataXgbPCFit
Dependencies:askpassbase64encbslibcachemclassclicolorspacecpp11crosstalkcurldata.tabledigestdplyre1071evaluatefansifarverfastmapFNNfontawesomefsgenericsggplot2gluegssgtablehighrhtmltoolshtmlwidgetshttrisobandjquerylibjsonlitekernlabKernSmoothknitrlabelinglaterlatticelazyevallifecyclemagrittrMASSMatrixmatrixStatsmemoisemgcvmimemixtoolsmunsellnlmeopensslpillarpkgconfigplotlypromisesproxypurrrR6rappdirsRColorBrewerRcppRcppArmadillorlangrmarkdownsassscalessegmentedstringistringrsurvivalsystibbletidyrtidyselecttinytexutf8vctrsviridisLitewithrxfunxgboostyaml
Readme and manuals
Help Manual
Help page | Topics |
---|---|
Additive Multiplicative Kernel Regression | AMK |
Power curve comparison | ComparePCurve |
Percentage weighted difference between power curves | ComputeWeightedDifference |
Covariate Matching | CovMatch |
Wind Energy data set containing 47,542 data points | data1 |
Wind Energy data set containing 48,068 data points | data2 |
Energy decomposition for wind turbine performance comparison | deltaEnergy |
Function comparison using Gaussian Process and Hypothesis testing | funGP |
Power imputation | imptPower |
KNN : Fit | KnnPCFit |
KNN : Predict | KnnPredict |
KNN : Update | KnnUpdate |
predict from temporal Gaussian process | predict.tempGP |
Smoothing spline Anova method | SplinePCFit |
SVM based power curve modelling | SvmPCFit |
Data synchronization | syncSize |
temporal Gaussian process | tempGP |
Updating data in a model | updateData |
Update the data in a tempGP object | updateData.tempGP |
xgboost based power curve modelling | XgbPCFit |